Hydrological simulation method for averagely fusing multi-source data based on Bayesian mode

Abstract

The invention discloses a hydrological simulation method based on Bayesian mode average fusion multi-source data. The method comprises the following steps of firstly, collecting meteorological data, hydrological series, satellite inversion rainfall and a reanalysis air temperature data set of a ground station in a scarce data area; respectively establishing correction models of ground observationdata and a simultaneous simulation meteorological data set in different months by adopting a quantile mapping-based daily deviation correction method, a regression correction method and an equal ratecorrection method; then adopting a seasonal Bayesian mode averaging method, optimizing the weight of each deviation correction scene through a posterior probability density function to acquire a corrected long-series meteorological data set; and calibrating a basin hydrological model and a long-short-term memory neural network model according to actual measurement data, and finally inputting the corrected long-series meteorological data set to realize runoff process simulation. Long-series runoff simulation of regions with scarce data can be realized, and an important reference basis with highoperability can be provided for basin water resource management and planning.

Description

technical field [0001] The invention relates to the technical field of hydrological simulation, in particular to a hydrological simulation method based on Bayesian mode average fusion of multi-source data. Background technique [0002] High-quality long-series precipitation and temperature data are important basic data for disaster early warning and control, agricultural production management, ecological protection, hydrological simulation of river basins, and planning and design of water conservancy projects. Traditional meteorological data mainly rely on station observations, but the network of meteorological stations is usually small in density and uneven in space, which makes it difficult to accurately reflect the temporal and spatial variation characteristics of meteorological variables, and cannot meet the needs of high-precision hydrological simulation and other engineering applications. [0003] In recent years, satellite telemetry technology and data inversion algor...

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Problems solved by technology

[0006] The present invention proposes a hydrological simulation method based on Bayesian model average fusion of multi-source data, which is used to solve or at least partially solve the technical problem of poor hydrological simulation effect existing in the methods in the prior art

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